Instructions to use liufanfanlff/C3-Context-Cascade-Compression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use liufanfanlff/C3-Context-Cascade-Compression with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="liufanfanlff/C3-Context-Cascade-Compression", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("liufanfanlff/C3-Context-Cascade-Compression", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use liufanfanlff/C3-Context-Cascade-Compression with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liufanfanlff/C3-Context-Cascade-Compression"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liufanfanlff/C3-Context-Cascade-Compression",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/liufanfanlff/C3-Context-Cascade-Compression

SGLang

How to use liufanfanlff/C3-Context-Cascade-Compression with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "liufanfanlff/C3-Context-Cascade-Compression" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liufanfanlff/C3-Context-Cascade-Compression",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "liufanfanlff/C3-Context-Cascade-Compression" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "liufanfanlff/C3-Context-Cascade-Compression",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use liufanfanlff/C3-Context-Cascade-Compression with Docker Model Runner:
```
docker model run hf.co/liufanfanlff/C3-Context-Cascade-Compression
```

Update model card: add pipeline, library tags, correct license, and fix paper link

by nielsr HF Staff - opened Nov 20, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-5

nielsr

Nov 20, 2025

This PR improves the model card for the C3-Context-Cascade-Compression model by:

Adding pipeline_tag: text-generation for better discoverability on the Hub, as the model performs text compression and decoding.
Adding library_name: transformers, enabling the automated "How to use" widget, as evidenced by the transformers library usage in the code snippet and config.json.
Updating the license from mit to apache-2.0, aligning with the code license explicitly specified in the GitHub repository.
Correcting the broken paper link in the main title to the official Hugging Face paper page.
Removing a redundant metadata block (--- license: mit ---) from the content section, as metadata should only appear in the YAML front matter.

These changes enhance the model's visibility and user experience on the Hugging Face Hub.

Update model card: add pipeline, library tags, correct license, and fix paper link218b4466

liufanfanlff changed pull request status to merged Nov 21, 2025

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment